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1.
Rev. Odontol. Araçatuba (Impr.) ; 43(3): 61-67, set.-dez. 2022. tab
Artigo em Português | LILACS, BBO - Odontologia | ID: biblio-1381326

RESUMO

Atualmente, o tratamento do ronco primário e da Síndrome da Apnéia/Hipopnéia Obstrutiva do Sono (SAHOS)1 através de aparelhos intra-orais (AIO) tem recebido a atenção dos pesquisadores pela comprovada eficácia destes dispositivos. Os aparelhos mais indicados são os reposicionadores de mandíbula que promovem um avanço mandibular, afastando os tecidos da orofaringe superior, o que evita a obstrução parcial ou total da área. Sua indicação é para casos de ronco primário e apnéias leves e moderadas2, no entanto é necessário que os candidatos apresentem número de dentes suficientes com saúde periodontal para a ancoragem do aparelho. Por ser uma doença de consequências sistêmicas graves, o tratamento da SAHOS é em sua essência de responsabilidade do médico especialista na área, porém o cirurgião dentista deve ter conhecimento para diagnosticar e tratar, quando o AIO for a opção terapêutica. A interpretação da polissonografia, exame que diagnostica e conduz para a escolha correta do tratamento, e dos dados cefalométricos são os principais quesitos ao Cirurgião Dentista que se propõe a tratar portadores da SAHOS. Nesse trabalho foi elaborado um questionário e aplicado aos cirurgiões dentistas de três diferentes cidades do Estado de São Paulo para que fosse possível avaliar o conhecimento desses profissionais a respeito do diagnóstico e tratamento da SAHOS. 70 Cirurgiões Dentistas foram entrevistados e os resultados mostraram que 70% destes têm interesse em trabalhar com os AIOs. Esse grupo se relacionou estatisticamente significante com aqueles que afirmaram já terem sido alguma vez questionado por algum paciente a respeito desse tratamento. Quanto à criação de uma especialidade para essa área, os profissionais da área de prótese e implante se mostraram mais interessados. E, do número total de entrevistados, apenas 25% já tiveram contato com esse tipo de aparelho, mas não conhece o protocolo de atendimento para o tratamento desses pacientes(AU)


Currently, the treatment of primary snoring and Obstructive Sleep Apnea/Hypopnea Syndrome (OSAHS)1 through intraoral appliances (OA) has received the attention of researchers due to the proven effectiveness of these devices. The most suitable devices are jaw repositioning devices that promote mandibular advancement, moving the tissues away from the upper oropharynx, which prevents partial or total obstruction of the area. Its indication is for cases of primary snoring and mild to moderate apnea2, however it is necessary that candidates have a sufficient number of teeth with periodontal health to anchor the appliance. As it is a disease with serious systemic consequences, the treatment of OSAHS is, in essence, the responsibility of the specialist in the area, but the dental surgeon must have the knowledge to diagnose and treat, when OA is the therapeutic option. The interpretation of polysomnography, na exam that diagnoses and leads to the correct choice of treatment, and cephalometric data are the main requirements for the Dental Surgeon who proposes to treat patients with OSAHS. In this work, a questionnaire was developed and applied to dentalsurgeons from three different cities in the State of São Paulo so that it was possible to assess the knowledge of these professionals regarding the diagnosis and treatment of OSAHS. 70 Dental Surgeons were interviewed and the results showed that 70% of them are interested in working with AIOs. This group had a statistically significant relationshipwith those who stated that they had already been asked by a patient about this treatment. Regarding the creation of a specialty for this area, professional in the area of ??prosthesis and implant were more interested. And, of the total number of respondents, only 25% have already had contact with this type of device, but do not know the care protocol for the treatment of these patients(AU)


Assuntos
Apneia Obstrutiva do Sono , Apneia Obstrutiva do Sono/terapia , Apneia Obstrutiva do Sono/diagnóstico por imagem , Modelos Dentários , Ronco , Polissonografia , Avanço Mandibular , Odontólogos
2.
PLoS One ; 17(9): e0272167, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36099242

RESUMO

Sleep apnea (SA) is a common disorder involving the cessation of breathing during sleep. It can cause daytime hypersomnia, accidents, and, if allowed to progress, serious, chronic conditions. Continuous positive airway pressure is an effective SA treatment. However, long waitlists impede timely diagnosis; overnight sleep studies involve trained technicians scoring a polysomnograph, which comprises multiple physiological signals including multi-channel electroencephalography (EEG). Therefore, it is important to develop simplified and automated approaches to detect SA. In the present study, we have developed an explainable convolutional neural network (CNN) to detect SA events from single-channel EEG recordings which generalizes across subjects. The network architecture consisted of three convolutional layers. We tuned hyperparameters using the Hyperband algorithm, optimized parameters using Adam, and quantified network performance with subjectwise 10-fold cross-validation. Our CNN performed with an accuracy of 69.9%, and a Matthews correlation coefficient (MCC) of 0.38. To explain the mechanisms of our trained network, we used critical-band masking (CBM): after training, we added bandlimited noise to test recordings; we parametrically varied the noise band center frequency and noise intensity, quantifying the deleterious effect on performance. We reconciled the effects of CBM with lesioning, wherein we zeroed the trained network's 1st-layer filter kernels in turn, quantifying the deleterious effect on performance. These analyses indicated that the network learned frequency-band information consistent with known SA biomarkers, specifically, delta and beta band activity. Our results indicate single-channel EEG may have clinical potential for SA diagnosis.


Assuntos
Eletroencefalografia , Síndromes da Apneia do Sono , Humanos , Redes Neurais de Computação , Polissonografia , Sono , Síndromes da Apneia do Sono/diagnóstico
3.
Ital J Pediatr ; 48(1): 173, 2022 Sep 15.
Artigo em Inglês | MEDLINE | ID: mdl-36109824

RESUMO

BACKGROUND: Healthy sleep is essential for the cognitive, behavioral and emotional development of children. Therefore, this study aimed to assess the behavioral consequences of sleep disturbances by examining children with sleep-disordered breathing compared with control participants. METHODS: Seventy-eight children with SDB (average age: 6.7 years (SD = 1.83); 61 had OSA and 17 had primary snoring) and 156 control subjects (average age: 6.57 years (SD = 1.46) participated in the study. We matched the groups in age (t(232) = 0.578, p = 0.564) and gender (χ2(1) = 2.192, p = 0.139). In the SDB group, the average Apnea-Hypopnea Index was 3.44 event/h (SD = 4.00), the average desaturation level was 87.37% (SD = 6.91). Parent-report rating scales were used to measure the children's daytime behavior including Attention Deficit Hyperactivity Disorder Rating Scale, Strengths and Difficulties Questionnaire, and Child Behavior Checklist. RESULTS: Our results showed that children with SDB exhibited a higher level of inattentiveness and hyperactive behavior. Furthermore, the SDB group demonstrated more internalizing (anxiety, depression, somatic complaints, social problems) (p < 0.001) and externalizing (aggressive and rule-breaking behavior) problems compared with children without SDB, irrespective of severity. CONCLUSIONS: Based on our findings we supposed that snoring and mild OSA had a risk for developing behavioral and emotional dysfunctions as much as moderate-severe OSA. Therefore, clinical research and practice need to focus more on the accurate assessment and treatment of sleep disturbances in childhood, particularly primary snoring, and mild obstructive sleep apnea.


Assuntos
Comportamento Problema , Síndromes da Apneia do Sono , Apneia Obstrutiva do Sono , Criança , Humanos , Polissonografia , Síndromes da Apneia do Sono/diagnóstico , Ronco
4.
Front Cell Infect Microbiol ; 12: 945284, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36105146

RESUMO

Objectives: The present study aimed to investigate the characteristics of salivary microbiota of children with obstructive sleep apnea (OSA) and to assess longitudinal alterations in salivary microbiota before and after adenotonsillectomy. Methods: A set of cross-sectional samples consisted of 36 OSA children (17 boys and 19 girls, 7.47 ± 2.24 years old) and 22 controls (9 boys and 13 girls, 7.55 ± 2.48 years old) were included in the study, among which eight OSA children (five boys and three girls, 8.8 ± 2.0 years old) who underwent treatment of adenotonsillectomy were followed up after 1 year. Saliva samples were collected, and microbial profiles were analyzed by bioinformatics analysis based on 16S rRNA sequencing. Results: In cross-sectional samples, the OSA group had higher α-diversity as estimated by Chao1, Shannon, Simpson, Pielou_e, and observed species as compared with the control group (p < 0.05). ß-Diversity based on the Bray-Curtis dissimilarities (p = 0.004) and Jaccard distances (p = 0.001) revealed a significant separation between the OSA group and control group. Nested cross-validated random forest classifier identified the 10 most important genera (Lactobacillus, Escherichia, Bifidobacterium, Capnocytophaga, Bacteroidetes_[G-7], Parvimonas, Bacteroides, Klebsiella, Lautropia, and Prevotella) that could differentiate OSA children from controls with an area under the curve (AUC) of 0.94. Linear discriminant analysis effect size (LEfSe) analysis revealed a significantly higher abundance of genera such as Prevotella (p = 0.027), Actinomyces (p = 0.015), Bifidobacterium (p < 0.001), Escherichia (p < 0.001), and Lactobacillus (p < 0.001) in the OSA group, among which Prevotella was further corroborated in longitudinal samples. Prevotella sp_HMT_396 was found to be significantly enriched in the OSA group (p = 0.02) with significantly higher levels as OSA severity increased (p = 0.014), and it had a lower abundance in the post-treatment group (p = 0.003) with a decline in each OSA child 1 year after adenotonsillectomy. Conclusions: A significantly higher microbial diversity and a significant difference in microbial composition and abundance were identified in salivary microbiota of OSA children compared with controls. Meanwhile, some characteristic genera (Prevotella, Actinomyces, Lactobacillus, Escherichia, and Bifidobacterium) were found in OSA children, among which the relationship between Prevotella spp. and OSA is worth further studies.


Assuntos
Microbiota , Apneia Obstrutiva do Sono , Criança , Pré-Escolar , Estudos Transversais , Feminino , Humanos , Masculino , Microbiota/genética , Polissonografia , Prevotella/genética , Estudos Prospectivos , RNA Ribossômico 16S/genética
5.
BMC Pulm Med ; 22(1): 352, 2022 Sep 18.
Artigo em Inglês | MEDLINE | ID: mdl-36115966

RESUMO

BACKGROUND: The proportion of patients with obstructive sleep apnea-hypopnea syndrome (OSAHS) is increasing year by year in China, which has become a major public health problem. Self-management of OSAHS and multiple support from caregivers are key to low hospital admissions and high quality of life for patients with OSAHS. Social support and health literacy are the main promoters of self-management behavior. However, their contributions have not been adequately studied. The purpose of this study is to investigate the level of self-management among patients with OSAHS and its relationship with general demographics, social support, and health literacy. METHODS: A total of 280 patients with OSAHS treated in two Classiii Grade A hospitals in Jinzhou City, Liaoning Province from October 2020 to July 2021 were selected as the study subjects. Patients were investigated by General Characteristics Questionnaire, Social Support Rating Scale (SSRS), Health Literacy Scale for Chronic Patients (HLSCP), and OSAHS Self-management Behavior Questionnaire, and the influencing factors of self-management of patients with OSAHS were analyzed. RESULTS: The average score of OSAHS self-management was 74.49(SD = 8.06), SSRS and HLSCP scores were positively correlated with total scores of self-management behavior. Furthermore, we found that disease duration, SSRS, and HLSCP scores were the main predictors of self-management behavior (R2 = 0.390, P < 0.001). CONCLUSION: This study found that OSAHS patients with a longer duration of disease and higher SSRS or HLSCP scores also had higher levels of self-management. The factors discussed in this study may be helpful in developing individualized interventions in self-management for patients with OSAHS.


Assuntos
Letramento em Saúde , Autogestão , Apneia Obstrutiva do Sono , Humanos , Polissonografia , Qualidade de Vida , Apoio Social , Síndrome
6.
BMC Pulm Med ; 22(1): 349, 2022 Sep 16.
Artigo em Inglês | MEDLINE | ID: mdl-36114522

RESUMO

BACKGROUND: The objective of this study was to evaluate the relationship between hyoid bone position and severity of obstructive sleep apnea (OSA), and to investigate its value as a complementary diagnostic method. METHODS: A total of 133 patients who were diagnosed as OSA with an apnea-hypopnea index ≥ 5 were included. Clinical examination, level I polysomnography (PSG) and lateral cephalographic analysis were done. Comprehensive PSG characteristics were compared according to hyoid bone position and the predictive power of the distance between the mandible and hyoid was assessed. RESULTS: The distance between the hyoid bone and mandibular plane was significantly longer in the severe OSA group (p = 0.013). The distance from hyoid bone to third vertebrae (C3) and hyoid bone to mentum were also longer in the severe OSA group but the difference did not reach statistical significance. The distance between hyoid bone and mandibular plane was effective in predicting severe OSA, with a cut-off value of 19.45 mm (AUC = 0.623, p = 0.040). When grouped according to a distance cut-off value of 19.45 mm, those with a longer distance between the hyoid bone and mandibular plane showed more respiratory disturbance, lower oxygen saturation levels, less deep slow wave sleep, and more fragmented sleep with arousals. CONCLUSIONS: The distance between the hyoid bone and mandibular plane derived from cephalometric analysis can be a valuable diagnostic parameter that can be easily applied in differentiating severe OSA patients.


Assuntos
Osso Hioide , Apneia Obstrutiva do Sono , Cefalometria/métodos , Humanos , Osso Hioide/diagnóstico por imagem , Polissonografia , Radiografia , Apneia Obstrutiva do Sono/diagnóstico por imagem
7.
Sci Rep ; 12(1): 15399, 2022 Sep 13.
Artigo em Inglês | MEDLINE | ID: mdl-36100642

RESUMO

Although recent studies have examined the bidirectional associations between physical activity and sleep parameters, few have focused on older adults utilizing objective assessments, such as polysomnography. This micro-longitudinal observational study included 92 Japanese older adults (aged 65-86 years) who underwent objective evaluations of sleep quality using polysomnography and completed subjective sleep-related questionnaires. Activity levels were assessed using an accelerometer. Polysomnography, subjective sleep-related questionnaires, and accelerometer were administered for 7 consecutive days. Multilevel models (participant-, day-level) were used to examine the temporal associations of objective and subjective sleep parameters with sedentary behavior and physical activity. In the day-level analysis, higher levels of sedentary behavior during daytime were associated with longer rapid eye movement (REM) sleep, shorter REM latency, lower levels of non-REM sleep (stage N3), and reduced delta power during daytime. Higher levels of low-intensity physical activity during daytime were associated with lower levels of REM sleep, longer REM latency, and increased stage N3 sleep in the day-level analysis. Higher levels of moderate-to-vigorous physical activity were associated with increased REM latency. Longer subjective sleep time was associated with increased next-day moderate-to-vigorous physical activity. Thus, low-intensity physical activity may provide objective benefits related to deep sleep parameters in older adults.


Assuntos
Distúrbios do Sono por Sonolência Excessiva , Sono , Idoso , Exercício Físico , Humanos , Análise Multinível , Polissonografia , Comportamento Sedentário
8.
Artigo em Inglês | MEDLINE | ID: mdl-36078611

RESUMO

The Cyclic Alternating Pattern (CAP) is a periodic activity detected in the electroencephalogram (EEG) signals. This pattern was identified as a marker of unstable sleep with several possible clinical applications; however, there is a need to develop automatic methodologies to facilitate real-world applications based on CAP assessment. Therefore, a deep learning-based EEG channels' feature level fusion was proposed in this work and employed for the CAP A phase classification. Two optimization algorithms optimized the channel selection, fusion, and classification procedures. The developed methodologies were evaluated by fusing the information from multiple EEG channels for patients with nocturnal frontal lobe epilepsy and patients without neurological disorders. Results showed that both optimization algorithms selected a comparable structure with similar feature level fusion, consisting of three electroencephalogram channels (Fp2-F4, C4-A1, F4-C4), which is in line with the CAP protocol to ensure multiple channels' arousals for CAP detection. Moreover, the two optimized models reached an area under the receiver operating characteristic curve of 0.82, with average accuracy ranging from 77% to 79%, a result in the upper range of the specialist agreement and best state-of-the-art works, despite a challenging dataset. The proposed methodology also has the advantage of providing a fully automatic analysis without requiring any manual procedure. Ultimately, the models were revealed to be noise-resistant and resilient to multiple channel loss, being thus suitable for real-world application.


Assuntos
Eletroencefalografia , Sono , Algoritmos , Nível de Alerta , Eletroencefalografia/métodos , Humanos , Polissonografia/métodos , Fatores de Tempo
9.
Sci Rep ; 12(1): 15086, 2022 Sep 05.
Artigo em Inglês | MEDLINE | ID: mdl-36064730

RESUMO

Much of our long-term knowledge is organised in complex networks. Sleep is thought to be critical for abstracting knowledge and enhancing important item memory for long-term retention. Thus, sleep should aid the development of memory for networks and the abstraction of their structure for efficient storage. However, this remains unknown because past sleep studies have focused on discrete items. Here we explored the impact of sleep (night-sleep/day-wake within-subject paradigm with 25 male participants) on memory for graph-networks where some items were important due to dense local connections (degree centrality) or, independently, important due to greater global connections (closeness/betweenness centrality). A network of 27 planets (nodes) sparsely interconnected by 36 teleporters (edges) was learned via discrete associations without explicit indication of any network structure. Despite equivalent exposure to all connections in the network, we found that memory for the links between items with high local connectivity or high global connectivity were better retained after sleep. These results highlight that sleep has the capacity for strengthening both global and local structure from the world and abstracting over multiple experiences to efficiently form internal networks of knowledge.


Assuntos
Aprendizagem , Sono , Humanos , Masculino , Polissonografia
10.
Sensors (Basel) ; 22(17)2022 Sep 04.
Artigo em Inglês | MEDLINE | ID: mdl-36081149

RESUMO

Heart rate (HR) and respiratory rate (RR) are two vital parameters of the body medically used for diagnosing short/long-term illness. Out-of-the-body, non-skin-contact HR/RR measurement remains a challenge due to imprecise readings. "Invisible" wearables integrated into day-to-day garments have the potential to produce precise readings with a comfortable user experience. Sleep studies and patient monitoring benefit from "Invisibles" due to longer wearability without significant discomfort. This paper suggests a novel method to reduce the footprint of sleep monitoring devices. We use a single silver-coated nylon fabric band integrated into a substrate of a standard cotton/nylon garment as a resistive elastomer sensor to measure air and blood volume change across the chest. We introduce a novel event-based architecture to process data at the edge device and describe two algorithms to calculate real-time HR/RR on ARM Cortex-M3 and Cortex-M4F microcontrollers. RR estimations show a sensitivity of 99.03% and a precision of 99.03% for identifying individual respiratory peaks. The two algorithms used for HR calculation show a mean absolute error of 0.81 ± 0.97 and 0.86±0.61 beats/min compared with a gold standard ECG-based HR. The event-based algorithm converts the respiratory/pulse waveform into instantaneous events, therefore reducing the data size by 40-140 times and requiring 33% less power to process and transfer data. Furthermore, we show that events hold enough information to reconstruct the original waveform, retaining pulse and respiratory activity. We suggest fabric sensors and event-based algorithms would drastically reduce the device footprint and increase the performance for HR/RR estimations during sleep studies, providing a better user experience.


Assuntos
Nylons , Taxa Respiratória , Frequência Cardíaca/fisiologia , Humanos , Polissonografia , Taxa Respiratória/fisiologia , Sono
11.
J Am Assoc Nurse Pract ; 34(9): 1083-1089, 2022 Sep 01.
Artigo em Inglês | MEDLINE | ID: mdl-36083320

RESUMO

BACKGROUND: Obstructive sleep apnea (OSA) is an independent and modifiable risk factor for atrial fibrillation (AF) and correlates with a three-fold higher risk of incident AF. Although OSA is prevalent in patients with AF, it remains underdiagnosed. Guidelines for OSA screening are ambiguous. LOCAL PROBLEM: A small community hospital in the southeast United States lacked standardized OSA screening and consistent sleep clinic referral for hospitalized patients with AF. METHODS: Over 3 months, an OSA bundle (including screening, education, and referral) was implemented for hospitalized patients with AF. A retrospective electronic health record (EHR) review established a baseline comparison group. Descriptive analyses between the intervention and comparison groups evaluated the effectiveness of the OSA bundle. INTERVENTIONS: Eligible patients received OSA screening with the STOP-Bang questionnaire. A STOP-Bang score of 3 or higher triggered patient education about the arrhythmogenic relationship of OSA and AF. At discharge, patients received an ambulatory sleep clinic referral. After 3 months, an EHR review assessed the rate of sleep clinic follow-up, sleep testing, OSA diagnosis, and initiation of positive airway pressure. RESULTS: Of the 68 patients in the comparison group and 33 patients in the intervention group, the rate of OSA screening increased from 4.4% to 100%. Sleep clinic referral increased from 66.7% to 93.5%. Sleep clinic follow-up increased from 0% to 10%. CONCLUSION: Screening for OSA and sleep clinic referral improved with the OSA bundle; however, sleep clinic follow-up remained low. Further quantitative and qualitative investigation is needed to better understand barriers to sleep clinic follow-up.


Assuntos
Fibrilação Atrial , Apneia Obstrutiva do Sono , Fibrilação Atrial/complicações , Fibrilação Atrial/diagnóstico , Humanos , Polissonografia , Estudos Retrospectivos , Apneia Obstrutiva do Sono/complicações , Apneia Obstrutiva do Sono/diagnóstico , Inquéritos e Questionários
12.
Annu Int Conf IEEE Eng Med Biol Soc ; 2022: 666-669, 2022 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-36085651

RESUMO

Although sleep apnea is one of the most prevalent sleep disorders, most patients remain undiagnosed and untreated. The gold standard for sleep apnea diagnosis, polysomnography, has important limitations such as its high cost and complexity. This leads to a growing need for novel cost-effective systems. Mobile health tools and deep learning algorithms are nowadays being proposed as innovative solutions for automatic apnea detection. In this work, a convolutional neural network (CNN) is trained for the identification of apnea events from the spectrograms of audio signals recorded with a smartphone. A systematic comparison of the effect of different window sizes on the model performance is provided. According to the results, the best models are obtained with 60 s windows (sensitivity-0.72, specilicity-0.89, AUROC = 0.88), For smaller windows, the model performance can be negatively impacted, because the windows become shorter than most apnea events, by which sound reductions can no longer be appreciated. On the other hand, longer windows tend to include multiple or mixed events, that will confound the model. This careful trade-off demonstrates the importance of selecting a proper window size to obtain models with adequate predictive power. This paper shows that CNNs applied to smartphone audio signals can facilitate sleep apnea detection in a realistic setting and is a first step towards an automated method to assist sleep technicians. Clinical Relevance- The results show the effect of the window size on the predictive power of CNNs for apnea detection. Furthermore, the potential of smartphones, audio signals, and deep neural networks for automatic sleep apnea screening is demonstrated.


Assuntos
Síndromes da Apneia do Sono , Smartphone , Algoritmos , Humanos , Redes Neurais de Computação , Polissonografia , Síndromes da Apneia do Sono/diagnóstico
13.
Annu Int Conf IEEE Eng Med Biol Soc ; 2022: 2611-2614, 2022 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-36085724

RESUMO

This work presents automated apnea event de-tection using blood oxygen saturation (SpO2) and pulse rate (PR), conveniently recorded with a pulse oximeter. A large, diverse cohort of patients (n=8068, age≥40 years) from the sleep heart health study dataset with annotated sleep events have been employed in this study. A deep-learning model is trained to detect apnea in successive 30 s epochs and performances are assessed on two independent sub-cohorts of test data. The proposed algorithm showcases the highest test performance of 90.4 % area under the receiver operating characteristic curve and 58.9% area under the precision-recall curve for epoch-based apnea detection. Additionally, the model consistently performs well across various apnea subtypes, with the highest sensitivity of 93.4 % for obstructive apnea detection followed by 90.5 % for central apnea and 89.1 % for desaturation associated hypopnea. Overall, the proposed algorithm provides a robust and sensitive approach for sleep apnea event detection using a noninvasive pulse oximeter sensor. Clinical Relevance - The study establishes high sensitivity for automated epoch-based apnea detection across a diverse study cohort with various comorbidities using simply a pulse oximeter. This highly cost-effective approach could also enable convenient sleep and health monitoring over long-term.


Assuntos
Aprendizado Profundo , Síndromes da Apneia do Sono , Adulto , Frequência Cardíaca , Humanos , Oxigênio , Saturação de Oxigênio , Polissonografia , Síndromes da Apneia do Sono/diagnóstico
14.
Annu Int Conf IEEE Eng Med Biol Soc ; 2022: 2961-2966, 2022 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-36085742

RESUMO

In this work we introduce a novel meta-learning method for sleep scoring based on self-supervised learning. Our approach aims at building models for sleep scoring that can generalize across different patients and recording facilities, but do not require a further adaptation step to the target data. Towards this goal, we build our method on top of the Model Agnostic Meta-Learning (MAML) framework by incorporating a self-supervised learning (SSL) stage, and call it S2MAML. We show that S2MAML can significantly outperform MAML. The gain in performance comes from the SSL stage, which we base on a general purpose pseudo-task that limits the overfitting to the subject-specific patterns present in the training dataset. We show that S2MAML outperforms standard supervised learning and MAML on the SC, ST, ISRUC, UCD and CAP datasets. Clinical relevance- Our work tackles the generalization problem of automatic sleep scoring models. This is one of the main hurdles that limits the adoption of such models for clinical and research sleep studies.


Assuntos
Generalização Psicológica , Medicina , Aclimatação , Humanos , Polissonografia , Sono
15.
Annu Int Conf IEEE Eng Med Biol Soc ; 2022: 3135-3138, 2022 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-36085914

RESUMO

High quality sleep monitoring is done using EEG electrodes placed on the skin. This has traditionally required assistance by an expert when the equipment needed to mounted. However, this creates a limitation in how cheap and easy it can be to record sleep in the subject's own home. Here we present a data set of 120 home recordings of sleep, in which subjects use self-applied ear-EEG monitoring equipment. We compare this data set to a previously recorded data set with both ear-EEG and polysomnography, which was applied by an expert. Clinical relevance - On all tested metrics, self applied sleep recordings behaved the same as expert applied. This indicates that ear-EEG can reliably be used as a home sleep monitor, even when subjects apply the equipment themselves.


Assuntos
Sono , Dispositivos Eletrônicos Vestíveis , Eletrodos , Eletroencefalografia , Humanos , Polissonografia
16.
Annu Int Conf IEEE Eng Med Biol Soc ; 2022: 4222-4225, 2022 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-36085969

RESUMO

Movements during sleep characterize sleep disorders, which can disturb sleep or its onset, impacting sleep quantity and quality. Video-polysomnography is the current gold standard to assess movements during sleep, but its availability is limited. Using data recorded with a 3D time of flight sensor, we developed a novel method of encoding temporal and spatial information of automatically identified movements during sleep. In a cohort of 20 insomnia patients and 18 controls, we showed that this novel method holds important information able to discriminate the groups. Future studies will explore the methodology in the context of other sleep disorders.


Assuntos
Distúrbios do Início e da Manutenção do Sono , Transtornos do Sono-Vigília , Humanos , Movimento , Polissonografia/métodos , Sono
17.
Annu Int Conf IEEE Eng Med Biol Soc ; 2022: 4942-4945, 2022 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-36085976

RESUMO

This work proposes a method utilizing only the submentalis EMG channel for the classification of sleep and wake states among the healthy individuals and patients with various sleep disorders such as sleep apnea hypopnea syndrome, dyssomnia, etc. We extracted autoregressive model parameters, discrete wavelet transform coefficients, Hjorth's complexity and mobility, relative bandpowers, Poincaré plot descriptors and statistical features from the EMG signal. We also used the energy of each epoch as a feature to distinguish between the sleep and wake states. Mutual information based feature selection approach was considered to obtain the top 25 features which provided maximum accuracy. For classification, we employed an ensemble of decision trees with random undersampling and boosting technique to deal with the class-imbalance problem in the sleep data. We achieved an overall accuracy of about 85% for the healthy population and about 70% on an average across different pathological groups. This work shows the potential of EMG chin activity for sleep analysis. Clinical Relevance- Automatic and reliable sleep-wake classification can reduce the burden of sleep experts in analyzing overnight sleep data (~ 8 hours) and also assist them to diagnose various neurological disorders at an early stage. Utilizing EMG channel provides an easier and convenient long-term recording of data without causing much disturbance in sleepunlike EEG which is inconvenient and hampers the natural sleep.


Assuntos
Apneia Obstrutiva do Sono , Fases do Sono , Humanos , Músculos , Polissonografia/métodos , Sono , Fases do Sono/fisiologia
18.
Annu Int Conf IEEE Eng Med Biol Soc ; 2022: 3665-3668, 2022 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-36086032

RESUMO

Actigraphy allows for the remote monitoring of subjects' activity for clinical and research purposes. However, most standard methods are built for proprietary measures from specific devices that are not widely used. In this study, we develop an algorithm for classifying sleep and awake using a single day of triaxial accelerometer data, which can be acquired from all smart devices. This algorithm consists of two stages, clustering and hidden Markov modeling, and outperforms standard algorithms in sensitivity (94%), specificity (93 %), and overall accuracy (93%) across seven subjects. This method can help automate actigraphy analyses at scale using widely available technology using even a single day's worth of data. Clinical Relevance- Automated monitoring of patients' activity at home can help track recovery trajectories after surgery and injury, disease progression, treatment response.


Assuntos
Actigrafia , Sono , Actigrafia/métodos , Algoritmos , Humanos , Polissonografia/métodos , Sono/fisiologia , Vigília/fisiologia
19.
Annu Int Conf IEEE Eng Med Biol Soc ; 2022: 1944-1947, 2022 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-36086100

RESUMO

Sleep state classification is essential for managing and comprehending sleep patterns, and it is usually the first step in identifying sleep disorders. Polysomnography (PSG), the gold standard, is intrusive and inconvenient for regular/long-term sleep monitoring. Many sleep-monitoring techniques have recently seen a resurgence as a result of the rise of neural networks and advanced computing. Ballistocardiography (BCG) is an example of such a technique, in which vitals are monitored in a contactless and unobtrusive manner by measuring the body's reaction to cardiac ejection forces. A Multi-Headed Deep Neural Network is proposed in this study to accurately classify sleep-wake state and predict sleep-wake time using BCG sensors. This method achieves a 95.5% sleep-wake classification score. Two studies were conducted in a controlled and uncontrolled environment to assess the accuracy of sleep-awake time prediction. Sleep-awake time prediction achieved an accuracy score of 94.16% in a controlled environment on 115 subjects and 94.90% in an uncontrolled environment on 350 subjects. The high accuracy and contactless nature make this proposed system a convenient method for long-term monitoring of sleep states, and it may also aid in identifying sleep stages and other sleep-related disorders. Clinical Relevance- Current sleep-wake state classification methods, such as actigraphy and polysomnography, necessitate patient contact and a high level of patient compliance. The proposed BCG method was found to be comparable to the gold standard PSG and most wearable actigraphy techniques, and also represents an effective method of contactless sleep monitoring. As a result, clinicians can use it to easily screen for sleep disorders such as dyssomnia and sleep apnea, even from the comfort of one's own home.


Assuntos
Balistocardiografia , Aprendizado Profundo , Transtornos do Sono-Vigília , Vacina BCG , Humanos , Polissonografia/métodos , Sono
20.
Annu Int Conf IEEE Eng Med Biol Soc ; 2022: 2941-2944, 2022 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-36086216

RESUMO

Rapid eye movement (REM) sleep behavior disorder (RBD) is parasomnia and a prodromal manifestation of Parkinson's disease. The current diagnostic method relies on manual scoring of polysomnograms (PSGs), a procedure that is time and effort intensive, subject to interscorer variability, and requires high level of expertise. Here, we present an automatic and interpretable diagnostic tool for RBD that analyzes PSGs using end-to-end deep neural networks. We optimized hierarchical attention networks in a 5-fold cross validation directly to classify RBD from PSG data recorded in 143 participants with RBD and 147 age-and sex-matched controls. An ensemble model using logistic regression was implemented to fuse decisions from networks trained in various signal combinations. We interpreted the networks using gradient SHAP that attribute relevance of input signals to model decisions. The ensemble model achieved a sensitivity of 91.4 % and a specificity of 86.3 %. Interpretation showed that electroencephalography (EEG) and leg electromyography (EMG) exhibited most patterns with high relevance. This study validates a robust diagnostic tool for RBD and proposes an interpretable and fully automatic framework for end-to-end modeling of other sleep disorders from PSG data. Clinical relevance- This study presents a novel diagnostic tool for RBD that considers neurophysiologic biomarkers in multiple modalities.


Assuntos
Aprendizado Profundo , Transtorno do Comportamento do Sono REM , Eletroencefalografia/métodos , Eletromiografia/métodos , Humanos , Polissonografia/métodos , Transtorno do Comportamento do Sono REM/diagnóstico
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